Location Quotient: Formula, Data Sources, and B2B Uses

The first territory map I ever built was wrong. Confidently, expensively wrong.

I was a few years into B2B marketing, working out of Hamburg, and I split the U.S. into four sales regions by drawing lines that looked balanced. Equal square miles. Roughly equal population. Felt fair. Felt smart.

It wasn’t. We sold to manufacturers, and I’d handed one rep a giant region that was mostly retail and tourism. Another rep got a tiny Midwest patch stuffed with exactly our buyers. Same effort, wildly different results. One quota looked like genius. The other looked like a firing.

So I went hunting for a number that shows where an industry ACTUALLY lives, not where it feels like it should. That number is the location quotient.

And once I learned to read it, I never designed a territory blind again. Here’s what I’ll cover. How the location quotient works, how to calculate it, where the clean data hides, and the mistakes I made so you can skip them. Let’s get into it. 👇

📌 TL;DR: Location quotient (LQ) compares how concentrated an industry is in one region against the whole country. LQ = regional industry share ÷ national industry share. An LQ of 1.0 is average. Above 1.2 means the region specializes (an export industry, great for targeting). Below 0.8 means it under-serves that industry (an import gap, sometimes an opening). Pull free employment data from BLS QCEW, Census County Business Patterns, or BEA, then use LQ to design territories, score leads, and size markets by geography instead of by gut.

What is a location quotient?

A location quotient is a ratio. It shows how concentrated an industry, occupation, or skill is in one region versus a larger reference area, usually the national economy. That’s the short answer.

Here’s the longer one. Raw job counts tell you how BIG something is. A location quotient tells you how SPECIAL it is. Los Angeles has a lot of restaurants, sure. But so does every huge metro. The real question is whether LA leans into film and entertainment more than the country as a whole. It does, dramatically. And that lean is exactly what a location quotient measures.

Think of it as a spotlight. It cuts through population size and points at what a place is unusually good at. When I first ran LQ across our target market, one mid-size metro stopped me cold. I’d basically ignored it. Turns out it concentrated our exact buyer at more than three times the national rate. Three times! That region had been sitting in a rep’s “maybe later” pile for a year.

The formula is a ratio of two ratios:

→ LQ = (regional industry employment ÷ total regional employment) ÷ (national industry employment ÷ total national employment)

So if healthcare is 15% of the jobs in a metro but only 10% of jobs nationally, the location quotient is 1.5. That region carries 50% more healthcare weight than the typical American place. Above average. Worth a look.

This geographic signal is one more layer of firmographic context you can fold into your account records, right next to industry codes and headcount. It’s the same instinct behind good Data Enrichment work: add a fact your competitors don’t have, and your targeting gets sharper.

How do you read a location quotient number?

You read it against 1.0, the national baseline. Anything above 1 means the region has more of that industry than average. Below 1, it has less. That’s the whole trick.

But the ranges matter more than the raw digits. Here’s the cheat sheet I keep pinned:

LQ range	What it signals	What I do with it
Below 0.8	Import gap. Region under-serves this industry	Check for greenfield demand or skip
0.8 to 1.2	Average. Matches the national mix	Treat as a normal territory
1.2 to 2.0	Real specialization. Above-average cluster	Prioritize for targeting
Above 2.0	Strong export cluster. Talent and suppliers concentrate here	Go hard, but check saturation

A quick honesty note. High LQ isn’t automatically a good territory. It means concentrated, not necessarily growing, and not necessarily winnable. I’ve watched a declining factory town hold a location quotient of 2.5 for years while it quietly lost jobs. High concentration, shrinking pie. Read the LQ, then check the trend and the raw size before you fall in love.

💡 Rule of thumb: LQ tells you WHERE an industry leans. It does not tell you how fast it's growing or how big the total prize is. Always pair it with absolute job counts and a growth trend before you move a rep.

How do you calculate a location quotient?

You calculate a location quotient with four data points and one division. Grab the regional industry jobs, total regional jobs, national industry jobs, and total national jobs, then divide the regional share by the national share.

I’ve built these by hand in a spreadsheet and inside automated pipelines chewing through millions of rows. Either way, the math never changes. Here are the steps:

• Step 1. Regional industry employment → jobs in your target industry inside the region.
• Step 2. Total regional employment → all jobs in that same region.
• Step 3. National industry employment → jobs in that industry across the whole country.
• Step 4. Total national employment → all jobs nationwide.
• Step 5. Regional share = Step 1 ÷ Step 2.
• Step 6. National share = Step 3 ÷ Step 4.
• Step 7. Location quotient = Step 5 ÷ Step 6.

Let’s run one so it sticks. Say a metro has 24,000 software jobs out of 600,000 total jobs. Nationally, say software runs about 4,000,000 out of 158,000,000.

• Regional share: 24,000 ÷ 600,000 = 0.04, so 4%
• National share: 4,000,000 ÷ 158,000,000 = 0.025, so about 2.5%
• Location quotient: 0.04 ÷ 0.025 = 1.6

An LQ of 1.6 means that metro carries 60% more software weight than the country does. For a company selling to software teams, that’s a territory you staff, not one you sample. One division. One clear answer.

And yes, keep the regional and national numbers from the same year, or the ratio turns to nonsense. Same period, same industry definitions, every time. Sloppy inputs here are a Data Quality problem, not a math problem. They’re also the number one reason people distrust their own maps.

Building your first LQ map in a spreadsheet

You don’t need a data science team for this. One spreadsheet, about an hour. Here’s the exact process I hand new analysts on my team:

• Pick one industry and one geography level. Say NAICS 5112 (software publishers) at the metro level. Resist the urge to do everything at once.
• Download two tables from BLS QCEW. One row per metro with your industry’s employment, plus each metro’s total employment. Then grab the national totals for the same year.
• Add four columns. Regional share, national share, LQ, and total industry jobs. The share columns are simple divisions; the LQ column divides one share by the other.
• Add a floor flag. A column that marks any metro with fewer than 200 industry jobs as “ignore,” so tiny-sample noise can’t fool you.
• Sort by LQ, then eyeball the total-jobs column. The winners are metros that are BOTH concentrated and big enough to matter.

That last step is the one people skip. It’s also the whole game. A metro with an LQ of 3.0 and 180 jobs is a rounding error. Meanwhile, a metro with an LQ of 1.6 and 24,000 jobs is a quarter of your pipeline. Sort by concentration, filter by size, and you’ve got a ranked target list before lunch.

Here’s what a slice of that finished sheet looks like:

Metro	Industry jobs	Local share	National share	LQ	Verdict
Metro A	24,000	4.0%	2.5%	1.60	Target: concentrated and big
Metro B	5,200	3.4%	2.5%	1.36	Solid secondary territory
Metro C	180	7.5%	2.5%	3.00	Ignore: too few total jobs
Metro D	9,800	1.8%	2.5%	0.72	Import gap: enter only if demand is real

Metro C is the trap. On paper it’s the most “specialized” place on the list. In practice it’s 180 jobs, which is a company or two. Numbers without a size check will send your best rep to a ghost town.

Where do you get the data for location quotient?

You get it one of two ways. Pull raw employment stats from free government sources and calculate LQ yourself. Or buy pre-calculated values from a data provider. I’ve done both. The right call depends on your budget, your team, and how custom you need to get.

Collecting the raw data yourself

This route is free and flexible. It’s where I’d start if anyone on your team is comfortable in a spreadsheet. The main sources:

• BLS QCEW (Quarterly Census of Employment and Wages) → county and metro employment by NAICS industry code. It covers about 95% of U.S. jobs and is the gold standard for industry LQ. The BLS location quotient explainer even publishes pre-computed LQ values in its data viewer.
• Census County Business Patterns (CBP) → establishment and employment counts down to county and ZIP level, handy when you need fine geography.
• Census American Community Survey (ACS) → occupation-based employment, so you can build OCCUPATION location quotients, not just industry ones.
• BEA → GDP by industry and by region, if you’d rather measure output than headcount.

A few pages worth bookmarking. The BEA explainer on location quotients walks through the concept in plain language, and the County Business Patterns program page shows exactly which geography levels you can pull. For occupation-based work, the American Community Survey is the one you want. And when you need to line up industry codes across sources, keep the NAICS code list open.

The catch with doing it yourself? Government data lags. QCEW releases months after a quarter ends, so you’re always looking slightly into the past. You’ll also spend real time aligning NAICS versions and cleaning suppressed cells. Budget for that.

One trap I hit early: the NAICS system gets revised every few years, and codes shift. If your regional table uses one vintage and your national table uses another, your shares won’t line up. Your LQ will quietly lie to you. Check the vintage on every file before you divide anything. It’s boring, and it’s the single most common reason a homemade LQ comes out wrong. Watch for suppressed cells too, the small industries where the government hides exact counts for privacy. Don’t treat a blank as a zero, or you’ll invent import gaps that aren’t real.

🧠 Watch the geography: The same industry can look concentrated at the metro level and totally average at the state level. Pick your geography before you calculate, and keep it consistent, or you'll compare numbers that don't mean the same thing.

Buying from a data provider

If you don’t want to build a stats pipeline, providers sell pre-calculated location quotients with regular updates and API access. You trade some customization and money for speed and clean delivery.

What you’re really paying for is coverage plus maintenance. Someone else normalizes the NAICS codes, refreshes the data, and hands you an API you can wire into enrichment. When you evaluate one, test their numbers against a cluster you already know is real (aerospace in Wichita, film in LA). If the known clusters don’t light up, walk away.

Whichever route you pick, plan to join this geographic layer onto your existing account records. That join lives or dies on Data Matching. Line up the wrong region to the wrong company and even perfect LQ data steers you wrong.

Export industries vs import industries

An export industry serves customers OUTSIDE its region and shows a location quotient above 1.2. Import industries mostly serve local needs and show an LQ below 0.8. The gap between those two ideas is where a lot of B2B strategy actually happens.

Here’s how I hold the two apart in my head.

Export industries (LQ above 1.2)

Export industries are the region’s specialty, the thing it makes and sends out into the world. When aerospace makes up 5% of a metro’s jobs but only 1% nationally, that’s an LQ of 5.0. The region clearly produces far more than it consumes and ships the rest elsewhere.

For targeting, export clusters are gold. The buyers are dense, the suppliers are dense, and the local talent already speaks your language. I flagged every metro with an LQ above 2.0 in our target industry. Those became our priority territories. Reps there got the most support and the tightest quotas. Concentrated prospects, developed ecosystem, less driving between meetings.

There’s a softer benefit too. In a real cluster, your reference stories travel. Sell one manufacturer in a dense metro and the next prospect down the road already knows that company, maybe shares suppliers, maybe poached an engineer from them. Word moves inside clusters in a way it never does across scattered accounts. So the same deal takes less convincing the second and third time. That compounding is invisible on a spreadsheet but very real in a pipeline. It’s a big part of why I lean into export regions.

Import industries (LQ below 0.8)

Import industries are under-represented locally, so the region leans on supply from elsewhere. When retail is 8% of local jobs versus 10% nationally, the LQ is 0.8. The place is likely buying from chains and online sellers based somewhere else.

Import gaps can be a trap or an opening. Sometimes a region under-serves an industry for a good structural reason, and you’d be fighting gravity to enter. But sometimes it’s genuine unmet demand with no entrenched competitor. One team I worked with entered a couple of low-LQ metros. They ramped noticeably faster than in mature clusters. Nobody had planted a flag yet. Read the reason behind the gap before you bet on it.

A real industry example

The clearest example I know is entertainment in Los Angeles. It makes the whole concept click.

The Los Angeles metro shows a location quotient well above 2.0 in motion picture and sound recording. That’s not an accident of size. It’s a genuine cluster: a century of talent, studios, financing, and infrastructure all pulling toward one industry. LA exports movies and shows to the entire planet.

Now compare it to a tourism economy. Las Vegas, Orlando, and Honolulu run high LQs in leisure and hospitality because visitors from everywhere pour money into hotels and entertainment. They export the EXPERIENCE. Meanwhile a manufacturing town like Detroit shows a low leisure-and-hospitality LQ, because it specializes in building things and imports its entertainment from elsewhere.

Same country. Wildly different local economies. If you sold event technology, you’d chase Vegas and Orlando, not Detroit. Sell factory automation and you’d flip that map completely. The location quotient tells you which map to draw.

Line up a few real specialties and the pattern jumps out:

Metro	Specialized industry	Roughly	Why it clusters there
Los Angeles	Motion picture and sound recording	LQ above 2.0	A century of studios, talent, and financing
Houston	Oil and gas extraction	Very high LQ	Energy infrastructure and headquarters
Las Vegas	Leisure and hospitality	High LQ	Tourism economy that exports the experience
Detroit	Motor vehicle manufacturing	High LQ	Legacy auto supply chain and engineering base

None of these are secrets. But the same math that confirms the obvious clusters also surfaces the non-obvious ones. Think of a mid-size metro quietly concentrating your exact buyer. Everyone else drives right past it.

Why do industries cluster in the first place?

Industries cluster because being near each other pays. That’s the short answer. And it’s worth understanding, because it tells you whether a high LQ is durable or fragile.

Three forces do most of the work. First, a labor pool: once film needs editors and grips, workers move to LA, and the deep talent bench pulls in even more studios. Second, supplier networks: the more automakers sit around Detroit, the more parts suppliers set up shop nearby, which makes the region even better for automakers. Third, knowledge spillover: people in the same industry bump into each other, swap ideas, and start companies down the street. Economists call this agglomeration. It’s why clusters reinforce themselves once they form.

Why does that matter to you? Because a high LQ backed by these forces is sticky and worth a long-term bet. A high LQ with none of them, say one giant employer that could relocate tomorrow, is a house of cards. When I find a cluster, I ask which of the three forces holds it together before I commit a territory to it. If the answer is “just one big company,” I keep my exposure light.

How I actually use location quotient for B2B targeting

The formula is easy. What nobody teaches is what to DO with it. Here are the three plays I run, in order of payoff.

1. Territory design

This is the big one, the mistake I opened this article with. Geography and headcount aren’t the split anymore. I rank every metro by its LQ in our target industry. Then I group high-LQ metros into the same territories and staff them heavier. Reps stop driving through empty regions to reach the one buyer hiding there. Quotas start reflecting reality.

Bad territory = equal square miles. Good territory = equal concentrated demand.

Back to my Hamburg-era disaster. When I finally re-cut those four regions by LQ instead of map lines, the whole picture flipped. The Midwest patch I’d handed off as a consolation prize held three of our top five clusters. Our “prize” territory was mostly retail and tourism with an LQ under 0.7 in our industry. I’d been rewarding the wrong rep and punishing the right one, purely because I confused big-on-a-map with dense-with-buyers. One re-rank fixed a problem I’d spent a year blaming on people.

2. Lead scoring

I add a small geographic weight to our lead score based on the LQ of the account’s metro. A prospect sitting inside a cluster (LQ above 1.5) gets a bump, because the odds they have budget, peers, and a real use case are higher. It’s a light touch, maybe a few points. But across thousands of leads it nudges reps toward the accounts most likely to close.

Keep it simple. Nothing fancy: LQ under 0.8 gets no points, 0.8 to 1.2 gets a couple, above 1.2 gets a handful more. The exact numbers matter less than the direction. You’re just tilting the queue so that when two leads look identical on firmographics, the one inside a real cluster rises first. Do that consistently and your reps spend their best hours where deals actually live.

3. Market sizing and TAM by geography

When leadership asks “how big is this market,” a single national number is almost useless for planning. Breaking your total addressable market down by regional concentration shows where the money actually sits and in what order to enter. Pair the LQ view with a proper TAM analysis and the “how big” answer comes with geography built in.

A quick way to make it concrete: take your top 20 metros by LQ, estimate the target companies in each, and stack them. Nine times out of ten, a handful of high-LQ metros hold well over half your realistic addressable market. That’s your entry order, right there. Instead of “the U.S. market is huge,” you get “these six metros are most of the winnable revenue, so we start here.” Leadership can plan against that.

Industry LQ vs occupation LQ (a distinction most guides skip)

Most people calculate location quotients for industries. But you can run the exact same math on OCCUPATIONS, and for some B2B products that’s the sharper signal.

Here’s the difference. Industry LQ tells you where companies in a sector cluster. Occupation LQ tells you where a type of WORKER clusters, using data like the Census ACS. If you sell a tool for data engineers, you don’t just care where tech companies sit. You care where data engineers actually concentrate, and those two maps aren’t identical. A big bank in Charlotte might employ more data engineers than a mid-size software shop three states over.

I learned this the slightly embarrassing way. We were selling a product bought by financial analysts, and I’d mapped territories by “finance industry” LQ. Fine, but the smarter cut was the occupation itself, which lit up a couple of insurance-heavy metros I’d underweighted. When your buyer is a role rather than a sector, run occupation LQ. Same seven steps, different data source.

Beyond the basic formula: five flavors of LQ

The version everyone learns uses employment. But the same ratio works on other inputs, and the right flavor depends on what you’re trying to see. Here are the four I reach for most:

Type of LQ	What it measures	Best when
Employment LQ	Where jobs in an industry concentrate	Territory design and general targeting
Occupation LQ	Where a type of worker concentrates	Your buyer is a role, not a sector
Output (GDP) LQ	Where an industry’s value-added concentrates	You care about spend, not headcount
Dynamic LQ	How concentration is changing over time	You want early warning, not a snapshot

A word on that last one, because it’s the most underused. Dynamic LQ compares the CHANGE in a region’s industry share to the change nationally. It answers a different question. Not “is this place specialized” but “is this place specializing MORE or less than everyone else.” A metro can have a modest LQ today and a steep upward dynamic LQ. That’s exactly the kind of emerging cluster you want to reach before your competitors notice it.

The fifth flavor is the one almost nobody mentions: establishment LQ. QCEW counts both jobs and ESTABLISHMENTS, meaning individual business locations, and you can build the ratio from either. Why care? Because a high employment LQ with a low establishment LQ means a few massive employers dominate the region. That’s an account-based, land-the-whale motion. A high establishment LQ means lots of smaller firms instead, which suits a volume sales play. Same region, same industry, two completely different go-to-market answers.

You don’t need all five. But knowing they exist means you stop forcing every question through the employment version when a better lens is one data source away.

Combine location quotient with growth, or you’ll get burned

A single LQ can’t tell a rising region from a falling one. So I never look at concentration alone. I plot it against growth on a simple two-by-two, and that little grid has saved me from more bad calls than any other tool.

	Growing employment	Shrinking employment
High LQ	Star cluster. Invest and defend	Fading cluster. Harvest, then diversify
Low LQ	Emerging market. Enter early	Weak market. Usually skip

The two boxes on the left are where you spend. Top-right is the sneaky one: still specialized, still impressive on an LQ table, but quietly losing jobs. That’s the fading factory town I keep warning about. And the bottom-left, low LQ but growing fast, is where the smartest early entries happen, because the cluster hasn’t formed yet and neither has the competition.

→ Concentration tells you WHERE. Growth tells you WHEN. You need both hands on the wheel.

The cleanest pairing I know: run shift-share analysis next to your LQ map. LQ shows the concentration; shift-share tells you whether the growth behind it is real or borrowed.

Other ways teams use location quotient

Targeting is my world, but LQ shows up in a lot of rooms. A few I’ve watched pay off:

• Site selection. Companies use industry LQ to pick where to open an office or plant, landing near a cluster with the right suppliers and talent already in place.
• Hiring and recruiting. Run occupation LQ to find the metros where your hard-to-hire roles actually live, then focus sourcing there instead of everywhere.
• Field marketing and events. Put your conference booth and dinners in high-LQ metros where your buyers concentrate, not just in the biggest cities.
• Economic development. Local agencies use LQ to name their region’s real strengths and pitch industries that fit, which is the original use the government sources were built for.
• Investment and risk. Analysts use LQ trends to spot regions over-exposed to a single fading industry before the downturn hits.

Same number, five different jobs. Once you can calculate it, you start seeing places to use it everywhere.

The limitations of location quotient (and the mistakes I made)

Location quotient is a great flashlight, but it’s a terrible flashlight if you point it wrong. Every one of these bit me at least once.

Small-number chaos. A tiny county with a few dozen jobs in an industry can post an absurd LQ off a handful of employees. Early on I got excited about a rural metro showing an LQ of 6. I told a rep to chase it. The whole market was maybe 150 jobs. Now I set a floor: at least 200 industry jobs before I trust the number at all.

Classification blur. Companies don’t fit neatly into one NAICS box. A tech firm might file as software, IT services, or data processing depending on how it reports revenue. Misfiled companies quietly warp the LQ, which is exactly why the underlying records need to be clean before you calculate anything.

Time lag. Government data trails reality by months, sometimes more than a year. When hospitality cratered during the pandemic, the LQ tables didn’t show it for a long while. If your industry moves fast, treat LQ as a rear-view mirror, not a windshield.

Concentration is not growth. I’ll say it again because it’s the sneakiest one. A dying industry can hold a high LQ for years while jobs bleed out. High and shrinking looks identical to high and thriving if concentration is all you check.

Boundary sensitivity. Your answer shifts with your geography. Metro versus county versus state can turn a strong cluster into an average one. Decide the boundary first, keep it consistent, and don’t switch it midway to make a story look better.

The remote-work skew. Employment data like QCEW counts jobs where the establishment sits. Residence-based data like the ACS counts workers where they LIVE. Remote work pulls those two maps apart, especially for tech roles. A metro can look stuffed with software talent while the employers, and the budgets, sit somewhere else entirely. So when you’re targeting companies, check that your source counts business locations, not bedrooms.

There’s a fancy fix for the small-number problem. Statisticians call it shrinkage. It nudges extreme values from tiny regions back toward 1.0, in proportion to how little data they rest on. You don’t need the math to use the idea. Just be more skeptical of a wild LQ the fewer jobs it’s built on, and more trusting of a modest LQ built on tens of thousands. Size buys confidence. A 200-job floor is the poor-person’s version of shrinkage, and honestly it catches most of the trouble.

📌 How to stay honest: Pair every location quotient with three things: a minimum job count, a multi-year trend, and the absolute market size. LQ finds candidates. Those three checks confirm them.

Spotting an export industry before it fades

Track the location quotient over time and you can catch a specialty slipping before it makes headlines. That’s the forward-looking move that turns LQ from a snapshot into an early warning.

A single-year LQ is a photo. A five-year LQ trend is a story. I watched one region’s manufacturing LQ slide from 2.8 down to 1.6 across a few years. Still specialized on paper. But clearly losing its edge. That trend told me to protect our existing customers there and diversify the pipeline before the decline hit our numbers. If I’d only looked at this year’s value, I’d have kept pouring reps into a fading cluster.

So calculate the current LQ and the trend together, always. Where a place is heading matters as much as where it stands.

How often should you recalculate?

Once a year is plenty for most B2B teams. Industry concentration moves slowly, and the underlying government data only refreshes annually anyway. Recalculating every month just gives you the same map with extra steps.

I run a full refresh once a year, usually when the new QCEW annual averages land, and I re-rank the whole target list against it. Then I only revisit mid-year if something big shakes a region: a major employer leaves, a plant opens, a whole sector wobbles. The point of LQ isn’t to chase weekly noise. It’s to keep your territories and target list pointed at reality as that reality drifts. A yearly tune-up does that without turning into a second job.

One habit that saves pain: keep last year’s LQ column next to this year’s in the same sheet. The difference between them IS your dynamic LQ, the change signal from earlier, and it falls out for free if you just don’t delete the old numbers.

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Frequently Asked Questions

What is the meaning of location quotient?

Location quotient is a ratio showing how concentrated an industry or occupation is in one region versus a larger reference area. An LQ of 1.0 means the region matches the national average. Above 1.0 means it specializes in that industry, and below 1.0 means it under-represents it. You calculate it by dividing the region’s share of jobs in an industry by the nation’s share of jobs in that same industry.

What does a location quotient of 1.5 mean?

A location quotient of 1.5 means the region has 50% more of that industry than the national average. If software is 6% of local jobs but only 4% nationally, the LQ is 1.5. That crosses into real specialization and usually flags a region worth targeting. Just check the absolute job count and the growth trend too, because a small town can post a high LQ off a tiny total market.

How do you calculate a location quotient?

Divide the region’s industry employment share by the nation’s industry employment share: LQ = (regional industry jobs ÷ total regional jobs) ÷ (national industry jobs ÷ total national jobs). For example, 24,000 software jobs out of 600,000 local jobs is a 4% share; nationally 4,000,000 out of 158,000,000 is about 2.5%; 0.04 ÷ 0.025 gives an LQ of 1.6. Keep both figures from the same year and the same industry definitions.

What is considered a high location quotient?

An LQ above 2.0 is generally considered high and signals a strong export cluster. Values from 1.2 to 2.0 show notable specialization, and anything above 2.0 usually means talent, suppliers, and buyers all concentrate in that region. But high doesn’t automatically mean opportunity. A declining industry can hold a high LQ while it loses jobs, so pair the number with a trend and the total market size.

Where can I find data to calculate location quotients?

Use free U.S. government sources: BLS QCEW for industry employment by county and metro, Census County Business Patterns for fine geography, Census ACS for occupations, and BEA for output-based measures. BLS QCEW is the standard for industry LQ, and its data viewer even publishes pre-computed location quotients. If you’d rather not build a pipeline, commercial providers sell pre-calculated values with API access, though you trade some flexibility and cost for the convenience.

What’s the difference between an export and an import industry?

An export industry serves customers outside the region and shows an LQ above 1.2, while an import industry mainly serves local needs and shows an LQ below 0.8. Export clusters are dense with buyers and suppliers, which makes them efficient to target. Import gaps can signal either a structural barrier to avoid or genuine unmet demand to enter, so always dig into why the gap exists before you commit resources.

Can a location quotient be too high?

Yes, an extremely high LQ can be misleading, usually because the region has very few total jobs in that industry. A rural county can post an LQ of 6 off a handful of employees, which looks like fierce specialization but is really small-sample noise. Very high LQ can also mean a market saturated with competitors. That’s why I set a minimum job count (around 200) before trusting any value, and I always read the LQ next to the absolute market size.

Is location quotient only used for jobs?

No, you can calculate a location quotient on any measurable input, not just employment. The most common version uses jobs, but you can build it from occupations, from GDP or output using BEA data, from wages, or from the number of establishments. Underneath, the formula never changes: divide the region’s share of the thing by the nation’s share of the thing. Just make sure your regional and national figures come from the same source and the same period.

It’s time to map your market by reality, not by gut

Here’s the honest takeaway. Most teams still draw territories, score leads, and size markets as if every region were a smaller copy of the country. They’re not. Each one leans into something, and the location quotient is the number that shows you what.

You now know the formula, how to read the ranges, where to pull clean data, and the traps that make LQ lie to you. That’s a real, usable skill, not a definition to memorize.

So start small. Pick your one target industry, pull employment for your top 20 metros from BLS QCEW, and calculate the LQ for each. Rank them. I’d bet money you’ll spot at least one region you’ve been under-serving and one you’ve been over-loving. That single afternoon changed how I ran my whole team, and it can do the same for yours.

And if you want the account records underneath your map cleaned and matched first, our guide to company data shows what solid firmographic ground looks like before you layer geography on top.

Go pull the numbers. You’ve got this. 👇